Zhiyu Shao

SWMM (Stormwater Management Model) is one of the most widely used computation tools in urban water resources management. Traditionally, the choice of rainfall data for calibrating the SWMM model has been arbitrary, lacking clarity on the most suitable rainfall types. In addition, the simplification in the SWMM hydrological module of the rainfall–runoff process, coupled with measurement errors, introduces a high level of uncertainty in the calibration. This study investigates the influences of rainfall types on the highly uncertain SWMM model calibration by implementing the Bayesian inference theory. A Bayesian SWMM calibration framework was established, in which an advanced DREAM(zs) (Differential Evolution Adaptive Metropolis, Version ZS) sampling method was used. The investigation focused on eight key hydrological parameters of SWMM. The impact of rainfall types was analyzed using nine rainfall intensities and three rainfall patterns. Results show that rainfall events equivalent to a one-year return period (R5, 42.70 mm total depth) or higher generally yield the most accurate parameters, with posterior distribution standard deviations reduced by 40–60% compared to low-intensity rainfalls. Notably, three parameters (impervious area percentage [Imperv], storage depth of impervious area [S-imperv], and Manning’s coefficient of impervious area [N-imperv]) demonstrated consistent accuracy irrespective of rainfall intensity, with a coefficient of variation below 0.05 for Imperv and S-imperv across all rainfall intensities. Furthermore, it was found that rainfall events with double peaks resulted in more satisfactory calibration compared to single or triple peaks, reducing the standard deviation of the Width parameter from 168.647 (single-peak) to 110.789 (double-peak). The findings from this study could offer valuable insights for selecting appropriate rainfall events before SWMM model calibration for more accurate predictions when it comes to urban non-point pollution control strategies and watershed management.

Urban roads can effectively handle peak flows during extreme storms by serving as surface flood passages, provided the flow remains within safety thresholds for vehicles and pedestrians. However, studies on flow allocation at crossroad intersections are limited. Previous research has overlooked important factors: road transverse slope and turning radius. This study built a “two in, two out” laboratory crossroad intersection to examine flow allocation patterns. Experiments explored the effects of road longitudinal slope, boundary conditions, and the combined influence of turning radius and side slope. The results indicated that at flatter slopes, flow allocation is more influenced by road slope, while at steeper slopes, the inflow Froude number ratio becomes more significant. The combined effect of the turning radius and side slope results in a flow allocation that differs by 44.3% compared to rectangular orthogonal channel intersections. A straightforward formula is proposed to calculate the flow allocation ratio based on experimental results and inflow power ratio. These findings could improve road intersection designs for better flood mitigation, offering a practical tool for planning flood-resilient road networks.

Urban road networks function as surface passage for floodwater transport during extreme storm events to reduce potential risks in the city. However, precise estimation of these flow rates presents a significant challenge. This difficulty primarily stems from the intricate three-dimensional flow fields at road intersections, which the traditional one-dimensional models, such as Storm Water Management Model (SWMM), fail to precisely capture. The two-dimensional and three-dimensional hydraulic models are overly complex and computationally intensive and thus not particularly efficient. This study addresses these issues by integrating a semiempirical flow diversion formula into the SWMM source code. The semiempirical formula, derived from hydraulic experiments and computational fluid dynamics simulations, captures the flow dynamics at T-shaped intersections. The modified SWMM’s performance was evaluated against experimental data, and the original SWMM, the two-dimensional MIKE21, and the three-dimensional FLUENT models. The results indicate that the modified SWMM matches the precision of the two-dimensional MIKE21, while significantly reducing computational time. Compared to MIKE21, this study achieved a Nash-Sutcliffe efficiency of 0.9729 and a root mean square error of 0.042, with computational time reduced by 99%. The modified SWMM is suitable for real-sized urban road networks. It provides a high-precision tool for urban road drainage system computation that is crucial for effective stormwater management.

In the context of the increasing frequency of urban flooding disasters caused by extreme weather, the accurate and timely identification and monitoring of urban flood risks have become increasingly important. This article begins with a bibliometric analysis of the literature on urban flood monitoring and identification, revealing that since 2017, this area has become a global research hotspot. Subsequently, it presents a systematic review of current mainstream urban flood monitoring technologies, drawing from both traditional and emerging data sources, which are categorized into sensor-based monitoring (including contact and non-contact sensors) and big data-based monitoring (including social media data and surveillance camera data). By analyzing the advantages and disadvantages of each technology and their different research focuses, this paper points out that current research largely emphasizes more “intelligent” monitoring technologies. However, these technologies still have certain limitations, and traditional sensor monitoring techniques retain significant advantages in practical applications. Therefore, future flood risk monitoring should focus on integrating multiple data sources, fully leveraging the strengths of different data sources to achieve real-time and accurate monitoring of urban flooding.

A major drainage systemwhich uses urban roadways as flood passages during extreme storm events has been proven to be effective and low-cost. However, to date, the amount of flow diverted at a bifurcating road crossing still could not be accurately assessed. Previous studies mainly focused on diversion flat bottom channels with no turning radius. Results from these studies are not applicable to the road crossing due to the significant discrepancies between the two flow fields. In this study, the flow distribution pattern at a roadway crossing with a T-shape was studied experimentally and numerically. The three-dimensional computational fluid dynamics (CFD) model was constructed using FLUENT. The numerical model was validated using the experimental results and showed satisfying agreements. Results of the 2^k factorial design experiments show that the flow distribution ratio is sensitive to turning radius, longitude slope, flowrate and downstream boundary conditions. Detailed numerical studies were conducted to reveal the relationship between the flow distribution ratio and the individual impacting factor. Findings from this research could provide more accurate flowrate predictions to guide the design of the road crossings to improve the performance of the major drainage system during extreme storms.